In droplet digital polymerase chain reaction (ddPCR), droplets pass by a sensor that detects whether a droplet includes a particular template nucleic acid sequence (e.g., DNA or RNA). As the droplets move by the sensor in time, a data signal is obtained. The signals from multiple droplets may overlap with each other, which can cause problems in determining a property of a particular droplet. Often, such droplets with overlapping signals are discarded.
Data in a series often consists of amplitude peaks, which may or may not be separated by some region of baseline level. If they are separated, they can usually be modeled by some function and analyzed by nonlinear regression or analyzed by peak identification methods, such as locating the extrema and associating peaks and troughs with peak locations and peak boundaries. When the peaks become close enough to overlap, this can become difficult to analyze since not only do the peaks occlude each other, they actually lift the adjacent peaks making it difficult to determine the appropriate baseline for the peak. Furthermore, if there are many peaks, typical regression methods may fail to converge. And, such regression methods require extracting all of the component peaks in order to examine any single peak. The performance cost from this may be prohibitive.